The selection of desired plant traits within plant breeding programs is typically based upon selection of one or more phenotypic traits. However, many important agronomic traits are complex, dramatically influenced by the environment, and are under polygenic control where a phenotypic trait is regulated by a plurality of genes, rather than by a single locus control. In traits under polygenic, or multigene control, the expression of alleles at many loci may contribute to the phenotype of interest.
Repeatedly, it has been shown that many genes fail to be expressed in response to developmental or environmental cues. An understanding of the factors that control the expression of these genes is important especially within transgenic organisms, for example, upon the introduction of a foreign gene to control its transcript level in a developmental, tissue or stress dependant manner. Such studies reveal the complexity and multi-level redundancy of controls that exist in the expression of genes. Mechanisms of control of gene expression can vary considerably between genes (see for example Hirt H. 1999 Trends Plant Sci. 4: 7-8). Under a given set of environmental or developmental conditions, genes involved in a given process do not always respond in a similar manner, nor do they accumulate in the same cell types or tissues indicating that they respond to different control or signal mechanisms.
Using current techniques, it is difficult to sort out key regulatory genes that control the expression of genes of interest among the whole cascade of events. Differentially expressed genes have been identified using approaches such as differential screening of cDNA libraries, genome sequencing combined with homology searches in gene banks, gene knock-out and complementation, mutation of homeotic genes, high throughput screening of ESTs using high density arrays of genes. These approaches, even though laborious, have occasionally been useful to identify genes involved in the regulation of gene expression but are not specifically designed for that purpose. Gene knock-out approaches (mutant variation) for the identification of factors controlling gene expression are suitable to small genome species such as Arabidopsis. Such techniques are also laborious, unspecific and often lead to undetectable or lethal phenotypes. Combination of techniques such as differential display of mRNA species, subtractive or normalized libraries and gene array can be used to single out regulatory genes, however these techniques are not specific to the analysis of the expression of a single gene and are time consuming. Moreover, with these techniques the transcripts of the regulatory genes of interest must be present in samples used to generate the libraries in order for the transcripts to be identified. The task can be overwhelming when multiple regulatory genes, with unsynchronised expression axe involved.
A quantitative trait locus (QTL) is a region of the genome that codes for one or more proteins and that explains a significant proportion of the variability of a given phenotype that may be controlled by multiple genes. Typically, one or more genetic markers have been used to identify a desired QTL. To date, most QTL studies of plant species have looked at gross morphological or agronomic phenotypes (e.g. yield, disease and stress resistance, time to flowering etc.). For instance, in WO 2000/18963 soybean plants comprising QTL associated with enhanced yields, and methods for selecting and breeding those plants is disclosed. This method involved the use of a specific marker nucleic acid capable of hybridizing to a second nucleic acid molecule that maps to specific region of Glycine soja and that is associated with enhanced yield. In U.S. Pat. No. 5,948,953 a QTL associated with brown stem rot (BSR) resistance in a soybean plant was identified. The QTL associated with BSR resistance may be used for plant selection using marker assisted selection. WO 99/31964 discloses the use of a marker nucleic acid that is genetically linked to a set of 63 specified loci for plant selection. The identified polymorphisms may be used in DNA fingerprinting and for mapping genes or QTLs associated with pest or disease resistance. Several recent QTL studies have explored the relationship between quantitative variation in specific metabolic changes including metabolite accumulation or changes in enzyme activities. For example, Byrne et al (Byrne, P. F., McCulen, M. D., Snook, M. B., Musket, T. A., Theuri, J. M., Widstrom, N. W., Wiseman, B. R. and E. H. Coe. 1996. Proc. Natl. Acad. Sci. 93: 8820-8825) disclose the mapping of a QTL accounting for 58% of the variance of the concentration of maysin, a flavone acting as a host-plant resistance factor against the corn earworm, to a locus encoding a transcription activator for portions of the flavonoid pathway. Prioul et al. (Priori, J.-L., Quarrie, S., Causse, M. and D. de Vienne. 1997. J. Exp. Bot. 48: 1151-1163; Prioul, J.-L., Pelleschi, S., Séne M., Thévenot, C, Causse, M, de Vienne, D. and A. Leonardi. 1999. J. Exp. Bot 50: 1281-1288) used enzyme activities, substrate and product levels of known biosynthetic pathway as quantitative traits in QTL analysis. Similarly, Pelleschi et al. (Pelleschi. S., Guy, S., Kim, J.-Y., Pointe, C., Mahé, A., Barthes, L., Leonardi, A., and J.-L. Prioul. 1999. Plant Mol. Biol. 39: 373-380) disclose the use of invertase activity as a marker for the identification of candidate genes for QTLs associated with variation in invertase activity in maize. Damerval et al. (Damerval C, Maurice, A., Josse, J. M, and D. de Vienne. 1994. Genetics 137: 289-201) discloses the use of peptides on a 2D-gel as a quantitative trait for QTL mapping.
None of the above approaches demonstrate or suggest that a QTL mapping approach can be used to identify genes involved in the regulation of expression of single genes or cascades of genes, nor do they suggest the use of using mRNA transcripts as the phenotypic trait for QTL mapping.
Dumas et al. (Dumas p., Sun Y., Corbeil G., Tremblay S., Pausova Z., Kren V., Krenova D., Pravenec M, Hamet P., and J. Tremblay 2000, J. Hypertens 18:545-551) disclose the use of mRNA as a phenotypic marker to map QTL associated with stress gene expression in rat. A related approach, using differential gene expression to map QTL associated with blood pressure in rats, was proposed by Cicila and Lee (Cicila G T. and S J Lee 1998 Hypertens Res 21: 289-296). Neither of these papers suggests a QTL approach for the identification of genes that are involved in the regulation of single genes or complex regulatory cascades responsible for controlling transcript levels. Furthermore, there is no discussion of QTL mapping of differential gene expression in organisms characterized as having a ploidy level greater than diploid, nor is there any discussion of identifying QTLs associated with differential gene expression in non-animal hosts.
The present invention is directed to the identification of genomic regions involved in the genetic regulation of the expression of one or more genes of interest associated with a desired trait. By using a QTL map-based analysis of the genome, loci for regulatory genes associated with the differential accumulation of transcripts or gene products are identified.
It is an object of the invention to overcome disadvantages of the prior art.
The above object is met by the combinations of features of the main claims, the sub-claims disclose further advantageous embodiments of the invention.